A1 Refereed original research article in a scientific journal
Continuous Radar-based Heart Rate Monitoring using Autocorrelation-based Algorithm in Intensive Care Unit
Authors: Seifizarei, Sepehr; Elnaggar, Ismail; Anzanpour, Arman; Sandelin, Jonas; Lahdenoja, Olli; Glassee, Miguel; Castro, Ivan D.; Torfs, Tom; van de Poll, Marcel C. G.; Airola, Antti; Kaisti, Matti; Koivisto, Tero
Publisher: IEEE
Publication year: 2025
Journal: IEEE Journal of Biomedical and Health Informatics
Journal name in source: IEEE Journal of Biomedical and Health Informatics
ISSN: 2168-2208
eISSN: 2168-2208
DOI: https://doi.org/10.1109/jbhi.2025.3527566(external)
Web address : https://ieeexplore.ieee.org/document/10834566(external)
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/477790260(external)
This study presents a radar-based algorithm for non-invasive heart rate monitoring in intensive care units (ICUs) using a 140 GHz Frequency-Modulated Continuous Wave (FMCW) radar, placed unobtrusively beneath hospital beds. Data were collected from 15 post-operative cardiac patients at Maastricht University Hospital, with an ECG device serving as the ground truth for validation. The proposed algorithm includes data preprocessing, channel selection, heart rate estimation, and post-processing, employing autocorrelation to detect rhythmic patterns and quality metrics to ensure reliable channel selection. The system achieved a mean absolute error (MAE) of 2.22 beats per minute (bpm) with 66% overall coverage, increasing to 98% during sinus rhythm periods. This approach demonstrates robust performance in challenging ICU environments by mitigating noise and motion artifacts and optimizing computational efficiency. These findings highlight the potential of radar-based systems to enhance patient care through continuous, non-invasive vital sign monitoring and validate the algorithm's effectiveness in real-world clinical scenarios.
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Funding information in the publication:
This study was conducted as part of the Moore4Medical Project (M4M), which received funding from the Electronic Components and Systems for European Leadership Joint Undertaking (ECSEL JU) in collaboration with the European Union’s H2020 Framework Programme (H2020/2014-2020) and National Authorities, under grant agreement H2020- ECSEL-2019-IA-876190. We appreciate the support provided by this initiative in enabling our research.